pandora.state_machine

This module contains class associated to the pandora state machine

Module Contents

Classes

PandoraMachine

PandoraMachine class to create and use a state machine

Attributes

FLAG_GRAPHVIZ

pandora.state_machine.FLAG_GRAPHVIZ = True

class pandora.state_machine.PandoraMachine

Bases: transitions.extensions.GraphMachine

PandoraMachine class to create and use a state machine

_transitions_run

_transitions_check

matching_cost_prepare(cfg: Dict[str, dict], input_step: str) → None

Matching cost computation :param cfg: user configuration :type cfg: dict :param input_step: step to trigger :type input_step: str :return: None

matching_cost_run(_: Dict[str, dict], __: str) → None

Matching cost computation :return: None

aggregation_run(cfg: Dict[str, dict], input_step: str) → None

Cost (support) aggregation :param cfg: pipeline configuration :type cfg: dict :param input_step: step to trigger :type input_step: str :return: None

semantic_segmentation_run(cfg: Dict[str, dict], input_step: str) → None

Building semantic segmentation computation :param cfg: pipeline configuration :type cfg: dict :param input_step: step to trigger :type input_step: str :return: None

optimization_run(cfg: Dict[str, dict], input_step: str) → None

Cost optimization :param cfg: pipeline configuration :type cfg: dict :param input_step: step to trigger :type input_step: str :return: None

disparity_run(cfg: Dict[str, dict], input_step: str) → None

Disparity computation and validity mask :param cfg: pipeline configuration :type cfg: dict :param input_step: step to trigger :type input_step: str :return: None

filter_run(cfg: Dict[str, dict], input_step: str) → None

Disparity filter :param cfg: pipeline configuration :type cfg: dict :param input_step: step to trigger :type input_step: str :return: None

refinement_run(cfg: Dict[str, dict], input_step: str) → None

Subpixel disparity refinement :param cfg: pipeline configuration :type cfg: dict :param input_step: step to trigger :type input_step: str :return: None

validation_run(cfg: Dict[str, dict], input_step: str) → None

Validation of disparity map :param cfg: pipeline configuration :type cfg: dict :param input_step: step to trigger :type input_step: str :return: None

run_multiscale(cfg: Dict[str, dict], input_step: str) → None

Compute the disparity range for the next scale :param cfg: pipeline configuration :type cfg: dict :param input_step: step to trigger :type input_step: str :return: None

cost_volume_confidence_run(cfg: Dict[str, dict], input_step: str) → None

Confidence prediction :param cfg: pipeline configuration :type cfg: dict :param input_step: step to trigger :type input_step: str :return: None

run_prepare(cfg: Dict[str, dict], left_img: xarray.Dataset, right_img: xarray.Dataset, scale_factor: None | int = None, num_scales: None | int = None) → None

Prepare the machine before running

Parameters:

• cfg (dict) – configuration

• left_img (xarray.Dataset) –

  left Dataset image containing :

  • im: 2D (row, col) or 3D (band_im, row, col) xarray.DataArray float32

  • disparity (optional): 3D (disp, row, col) xarray.DataArray float32

  • msk (optional): 2D (row, col) xarray.DataArray int16

  • classif (optional): 3D (band_classif, row, col) xarray.DataArray int16

  • segm (optional): 2D (row, col) xarray.DataArray int16

• right_img (xarray.Dataset) –

  right Dataset image containing :

  • im: 2D (row, col) or 3D (band_im, row, col) xarray.DataArray float32

  • disparity (optional): 3D (disp, row, col) xarray.DataArray float32

  • msk (optional): 2D (row, col) xarray.DataArray int16

  • classif (optional): 3D (band_classif, row, col) xarray.DataArray int16

  • segm (optional): 2D (row, col) xarray.DataArray int16

• scale_factor (int or None) – scale factor for multiscale

• num_scales (int or None) – scales number for multiscale

Returns:

None

run(input_step: str, cfg: Dict[str, dict]) → None

Run pandora step by triggering the corresponding machine transition

Parameters:

• input_step (str) – step to trigger

• cfg (dict) – pipeline configuration

Returns:

None

run_exit() → None

Clear transitions and return to state begin

Returns:

None

matching_cost_check_conf(cfg: Dict[str, dict], input_step: str) → None

Check the matching cost configuration

Parameters:

• cfg (dict) – configuration

• input_step (string) – current step

Returns:

None

disparity_check_conf(cfg: Dict[str, dict], input_step: str) → None

Check the disparity computation configuration

Parameters:

• cfg (dict) – configuration

• input_step (string) – current step

Returns:

None

filter_check_conf(cfg: Dict[str, dict], input_step: str) → None

Check the filter configuration

Parameters:

• cfg (dict) – configuration

• input_step (string) – current step

Returns:

None

refinement_check_conf(cfg: Dict[str, dict], input_step: str) → None

Check the refinement configuration

Parameters:

• cfg (dict) – configuration

• input_step (string) – current step

Returns:

None

aggregation_check_conf(cfg: Dict[str, dict], input_step: str) → None

Check the aggregation configuration

Parameters:

• cfg (dict) – configuration

• input_step (string) – current step

Returns:

None

semantic_segmentation_check_conf(cfg: Dict[str, dict], input_step: str) → None

Check the semantic_segmentation configuration

Parameters:

• cfg (dict) – configuration

• input_step (string) – current step

Returns:

None

optimization_check_conf(cfg: Dict[str, dict], input_step: str) → None

Check the optimization configuration

Parameters:

• cfg (dict) – configuration

• input_step (string) – current step

Returns:

None

validation_check_conf(cfg: Dict[str, dict], input_step: str) → None

Check the validation configuration

Parameters:

• cfg (dict) – configuration

• input_step (string) – current step

Returns:

None

multiscale_check_conf(cfg: Dict[str, dict], input_step: str) → None

Check the disparity computation configuration

Parameters:

• cfg (dict) – disparity computation configuration

• input_step (string) – current step

Returns:

None

cost_volume_confidence_check_conf(cfg: Dict[str, dict], input_step: str) → None

Check the confidence configuration

Parameters:

• cfg (dict) – configuration

• input_step (string) – current step

Returns:

None

check_conf(cfg: Dict[str, dict], img_left: xarray.Dataset, img_right: xarray.Dataset, right_left_img_check: bool = False) → None

Check configuration and transitions

Parameters:

• cfg (dict) – pipeline configuration

• img_left (xarray.Dataset) – image left with metadata

• img_right (xarray.Dataset) – image right with metadata

• right_left_img_check (bool) – if right image has been checked

Returns:

None

remove_transitions(transition_list: List[Dict[str, str]]) → None

Delete all transitions defined in the input list

Parameters:

transition_list (dict) – list of transitions

Returns:

None

is_not_last_scale(_: str, __: Dict[str, dict]) → bool

Check if the current scale is the last scale :param cfg: configuration :type cfg: dict :param input_step: current step :type input_step: string :return: boolean

static check_band_pipeline(band_list: numpy.ndarray, step: str, band_used: None | str | List[str] | Dict) → None

Check coherence band parameter between pipeline step and image dataset

Parameters:

• band_list (numpy.ndarray with bands) – band names of image

• step (str) – pipeline step

• band_used (None, str, List[str] or Dict) – band names for pipeline step

Returns:

None